基于历史预报的四维变分资料同化（4DVar）方法中的滤波

基于历史预报的四维变分同化方法在降维的样本空间最小化代价函数，避免切线性伴随模式，是一种比较经济的算法。但是因为选取的集合样本不可能无限多，实际样本数远远小于观测资料数以及模式变量的自由度，会导致观测站点和模式格点间产生虚假的相关。介绍了在历史预报4DVar中引入的局地化滤波技术，并通过3组试验，比较了局地化前后的结果。试验结果表明:引入局地化技术后，其能有效滤去初始场中的虚假相关关系，同时由于Schur算子的作用，滤波后的分析场是光滑连续的。而且实际个例研究结果表明，局地化后能改进6 h和12 h累积降水的均方根误差，进一步提高预报效果。  

Distance Dependent Filtering in a 4DVar Based on Historical Forecast Ensemble

An economical approach to implement the variational data assimilation(4Dvar) using the technique of Historical Sample Projection(HSP) is proposed,it is based on dimension reduction using an ensemble of historical samples to define a subspace,directly obtains an optimal solution in the reduced space and does not require implementation of the adjoint of tangent linear approximation.But the ensemble is composed of far fewer members than both the number of observational data and the degrees of freedom of the mo...